Drivers' cognitive load detection through ocular metrics classification for the development on Driver Monitoring Systems

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Vehicle automation can make roads safer by reducing the number of collisions attributed to human factors like distraction. However, despite the technological advancements observed over the last decade, vehicles requiring absolutely no input from the human driver have not yet reached widespread adoption. Available vehicle automation (Society of Automotive Engineers (SAE) level-2 and 3 systems; L2/L3) still requires the human to stay engaged in the overall task of driving either to maintain safe vehicle operations or to take over control whenever necessary. To ensure that these requirements are met, Driver Monitoring Systems (DMS) are being developed with the overarching goal of monitoring the state of the human driver. Relating to the potential for DMS to improve road safety, there remains the question of how accurately these systems can monitor the driver's cognitive load during L2/L3 driving and detect the onset of dangerous conditions like distraction. Thus, this research aims to develop more accurate DMS that are capable of detecting changes in drivers' cognitive load during L2/L3 driving. The research is articulated in three distinct objectives. Objective 1 will adopt ground-truth measures to test the accuracy of non-intrusive ocular metrics (pupil size, gaze concentration, blink rate) in detecting driver's cognitive load. Objective 2 will use ocular metrics to monitor changes in driver's cognitive load during simulated and on-road L2/L3 driving. Objective 3 will develop and test the accuracy of classification models in distinguishing between diverse levels of cognitive load (low to high) during L2/L3 driving. The impact of this project is twofold. DMS development. The applicant has existing collaborations with automotive suppliers (Dreyev, Faurecia) which are involved in the development of DMS. Data collected in this project will be used as a benchmark to help industry partners test the reliability of their DMS solutions. Classification models adopting diverse combinations of ocular metrics will also inform the development of more accurate cognitive load detection systems. L2/L3 policy development. The applicant is an active participant in industry organizations in the area of autonomous vehicles (ISO/Transport Canada, SAE) and has an active collaboration with the Ontario Ministry of Transportation. Project findings will be leveraged to inform the development of road safety policies for the safe adoption of L2/L3 automated driving systems. The development of new policies and accurate DMS will lead to safer vehicles and streets for all Canadians. The HQP trained in this research program will have highly sought-after skills and be able to contribute to this fast-paced emerging sector whether in academia, government, or industry.